Recent ground surface warming and its effects on permafrost on the central Qinghai-Tibet Plateau

Authors

  • Dr Tonghua Wu,

    Corresponding author
    1. Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    • Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China.
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  • Lin Zhao,

    1. Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
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  • Ren Li,

    1. Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
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  • Qinxue Wang,

    1. Center for Regional Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba 305-8506, Japan
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  • Changwei Xie,

    1. Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
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  • Qiangqiang Pang

    1. Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
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Abstract

In this study, the ground surface temperature (GST) records from 16 meteorological stations, which are located in or adjacent to permafrost regions on the central Qinghai-Tibet Plateau (QTP), are analysed using Mann–Kendal test and Sen's slope estimate methods. We revealed that the GSTs have shown statistically significant warming. On average, mean annual ground surface temperature has increased at a rate of 0.60 °C decade−1 over the period of 1980–2007, which is more pronounced than the increase of mean annual air temperature on the plateau. The winter ground surface warming is especially prominent, which is similar to the seasonal trends in changes of air temperature. As important parameters to assess the changes of ground thermal regime in cold regions, surface freezing and thawing indices were also studied. The nonparametric statistic test and estimate indicate that surface freezing and thawing indices both show significant variations (−111.2 and 125.0 °C d decade−1, respectively) on the central QTP. The intensive ground surface warming is responsible for the concurrent increase in permafrost temperatures at the long-term observation sites on the plateau. The close correlations between ground surface and permafrost temperatures indicate that the dramatic ground surface warming could have significant influence on the change of permafrost thermal regime in the study region. Copyright © 2012 Royal Meteorological Society

1. Introduction

The Qinghai-Tibet Plateau (QTP) is considered as the Third Pole in the world because its average elevation exceeds 4000 m above the sea level (Qiu, 2008). Owing to its unique and extremely high altitude, a vast expanse of cryosphere exists on the QTP primarily includes a large proportion of mountain glaciers in the world and extensive permafrost whose area approximately amounts to 1.5 million km2 (Zhou et al., 2000; Li et al., 2008). The QTP plays a crucial role for providing water resources to most of the Asian continent through cryospheric changes, which could supply the runoff, therefore impacting the livelihood of over 3.7 billion people (Zhang, 2007; You et al., 2010). Previous studies confirmed that QTP plays an important role on Asian climate and is one of the most sensitive regions to recent climatic changes (Yanai and Li, 1994; Liu and Chen, 2000; Sato and Kimura, 2007; Wu et al., 2007; Liu et al., 2009; Kang et al., 2010). The linear rate of air temperature increase amounts to 0.16 °C decade−1 for the mean annual and 0.32 °C decade−1 for the mean winter respectively on the QTP over the period 1955–1996, which indicate that the QTP has warmed at a greater rate than other regions in Northern Hemisphere and the same latitudinal zone during the same period (Liu and Chen, 2000). The air temperature averaged over 90 weather stations with elevations exceeding 2500 m has increased by 0.36 °C decade−1 during the period from 1961 to 2007, which equals to twice of Liu and Chen's estimation in 2000 (Wang et al., 2008). On the basis of statistical analysis of mean annual air temperature data from 43 stations with available humidity data, Rangwala et al. (2009) revealed that there was a statistically significant warming trend of 0.24 °C decade−1 on the QTP between 1961 and 2000 and that the winter and autumn warming was also prominent on the QTP. Lu and Liu (2010) analysed the changes of mean annual air temperatures at 69 meteorological stations and concluded that mean annual air temperature on the QTP has increased at a rate of 0.265 °C decade−1 from 1961 to 2005. Although various estimation of the increasing trend for air temperature was reported based on adopting diverse station numbers and applying instrumental records over different periods, the large magnitude of climatic warming occurring on the QTP has been accepted as a scientific fact.

As a consequence of pronounced climatic warming on the QTP, noticeable atmospheric and terrestrial changes have occurred during the last several decades (Duan et al., 2006; Cui and Graf, 2009; Sheng and Yao, 2009; Harris, 2010). Permafrost is the ground that remains at or below 0 °C for two or more years, underlies about three fifth of QTP area. The active layer is defined as the top layer of ground subject to seasonal freezing and thawing in permafrost regions. Recent measurements of ground temperatures on the QTP indicate that permafrost degradation has been occurring extensively since the 1970s (Jin et al., 2000; Cheng and Wu, 2007; Yang et al., 2010; Zhao et al., 2010). There are also studies that indicated that the mean annual soil temperatures at the depth of 1 m have increased by about 0.12–1.65 °C with an average increase of 0.91 °C from 1996 to 2006 in permafrost regions of the QTP (Wu and Zhang, 2008). The mean increasing rate of active layer thickness approximates to 7.5 cm year−1 on the QTP over the period 1995–2007 (Wu and Zhang, 2010). The extensive permafrost degradation on the QTP is expected to exert profound influence on the land surface energy and moisture balance, hydrological cycles, carbon exchange between the atmosphere and the earth surface, and ecosystem dynamics (Zhang et al., 2005; Cheng and Wu, 2007).

Ground surface is the interface where energy and mass exchanges and balances between earth and atmosphere. Most of fundamental energy and water cycles of the earth-atmosphere system occur at the ground surface. Ground surface temperature (0 cm at earth surface, thereafter GST) is of great importance for monitoring of the energy exchange between the ground surface and atmosphere in terms of sensible and latent heat fluxes (Oku et al., 2006). GST integrates climatic elements (such as air temperature and seasonal snow cover) and their interactions with ground surface characteristics (such as vegetation, surface soil texture, and terrain). Guglielmin (2006) revealed a very close relationship existed between the GST and air temperature with R2 of 0.93 for 1996–2002 at Boulder Clay in Antarctica. The change of GST also could be considered as a good indicator of climate change, as well as the variation of air temperature. It is important to quantify the rate of ground surface warming and estimate its spatial pattern. As far as the geocryological studies are concerned, long-term GST records are critical to estimate spatial distribution of frozen ground. GST is considered as an important upper boundary condition parameter for modelling and predicting the changes of thermal regime of permafrost. In permafrost regions, the annual ground surface freezing and thawing index calculated from GST observation data can be directly used to predict and map permafrost distribution and to estimate active layer thickness in cold regions (Nelson and Outcalt, 1987; Nelson et al., 1997; Zhang et al., 2005; Frauenfeld et al., 2007), although GST is not a standard meteorological measurement and is rarely available for a specific case. Most of previous studies focused on the studies of air temperature trends on the QTP, while there are relatively few studies in literature on studying the trend of instrumental records of GST and exploring the relationship between GST and permafrost temperatures in permafrost regions of the QTP because of scarcity of meteorological stations and lack of consecutive instrumental records for a long term over there.

Surface freezing/thawing index is defined as the number of the cumulative degree-days of the mean daily GST below/above 0 °C within a year, respectively. The long-term surface freezing/thawing index is reminiscent of cold/warm season temperature climatology. Similar to air freezing index, surface freezing index is of great importance to determine the ground freezing potential of a given climate and applied extensively for estimating the effect of freezing conditions upon the engineering infrastructure in cold regions (Steurer, 1996; Frauenfeld et al., 2007). Surface thawing index is quite useful for estimating active layer thickness in permafrost regions (Zhang et al., 1997, 2005; Nelson et al., 1997; Hinkel and Nelson, 2003). Both surface freezing and thawing indices are extensively applied to predict spatial distribution of permafrost and provide implication for engineering design in cold regions (Frauenfeld et al., 2007). Until now there are relatively few literature discussing the changes in surface freezing/thawing index while many early literature focused on the variations of air freezing/thawing index (Zhang et al., 1997; Frauenfeld et al., 2007; Wu et al., 2011). Accordingly, an understanding of the historical changes in the GST and ground freezing/thawing conditions is of great significance for forecasting the changes of ground thermal regime in cold regions.

There are few literatures depicting the long-term variations of GST and surface freezing/thawing index on the QTP. Also there are still uncertainties related to the relationship between GST and permafrost temperatures. The main purpose of this study is to adopt the available GST and corresponding surface freezing/thawing index time series to detect the trends of GST and surface freezing/thawing index in permafrost regions of the central QTP during the period 1980–2007. Spatial and temporal variability of the changes in GST and surface freezing/thawing index are also discussed in this study. In addition, the relationship between ground surface and permafrost temperatures change will be also analysed by analysing concurrent and consecutive observation records. The analysis will hopefully enhance our understanding of the characteristics of recent ground surface warming and contribute to providing important implication to interpret and predict possible response of permafrost to climatic warming on the QTP.

2. Data and methodology

On the QTP, the systematic observations of GST at most national-level meteorological stations were started from 1980 and the time series data of GST at some stations are discontinuous. Among the daily dataset for GST from 71 stations over the QTP provided by the National Climate Center, China Meteorological Administration, only few stations have consecutive records of daily GST. We selected 16 meteorological stations whose GST records are available and relatively complete from 1980 to 2007 in or adjacent to permafrost regions on the central QTP (Figure 1). The 16 stations are all above 3500 m a.s.l. with altitudes ranging from 3648 to 4800 m (Table I). Tuotuohe, Qingshuihe, and Yushu are located in the source regions of Yangtze River (Figure 1), where the eco-environment is exceedingly vulnerable to climatic changes (Ding et al., 2003). The daily temperature data were averaged to obtain monthly mean temperatures. The monthly mean temperatures were averaged over a 12-month annual period to obtain mean annual ground surface temperature (MAGST). Meanwhile, the seasonal GST for spring (March to May), summer (June to August), autumn (September to November), and winter (December to February) were obtained from the monthly data. The data were subject to routine quality control to eliminate any spurious values referring to the methods of data quality assessment and error correction introduced in Liu et al. (2006). Among the daily GST dataset, only the data of Yushu over the period January 12 to 21 February 2004 and of Maduo over the period April 1 to 17 May 2004 are missing. The missing data are extrapolated by using the daily air temperature time series at the same station over the period 2003–2005 by means of linear regression methods with a strong correlation of R2 = 0.93 for Yushu and R2 = 0.95 for Maduo, respectively at the 99% confidence level.

Figure 1.

Map showing the permafrost distribution on the QTP and the geographic locations of the selected 16 meteorological stations and two permafrost monitoring sites with available long-time-series data on the central QTP. The upper reach of Yangtze River was also shown in bold line in the map. This figure is available in colour online at wileyonlinelibrary.com/journal/joc

Table I. List of the selected stations above 3600 m above sea level in the eastern and central QTP, including the World Meteorological Organization (WMO) number, station name, latitude, longitude, and elevation
WMO numberStation nameLatitude (N)Longitude (E)Elevation (m)
  1. The GST data are all available from 1980 to 2007.

52908Wudaoliang35°13′93°05′4612.2
55279Bange31°23′90°01′4700.0
55294Amdo32°21′91°06′4800.0
55299Naqu31°29′92°04′4507.0
55493Dangxiong30°29′91°06′4200.0
55591Lhasa29°40′91°08′3648.7
56004Tuotuohe34°13′92°26′4533.1
56018Zaduo32°54′95°18′4066.4
56021Qumalai34°08′95°47′4175.0
56029Yushu33°01′97°01′3681.2
56033Maduo34°55′98°13′4272.3
56034Qingshuihe33°48′97°08′4415.4
56038Shiqu32°59′98°06′4200.0
56046Dari33°45′99°39′3967.5
56106Suoxian31°53′93°47′4022.8
56116Dingqing31°25′95°36′3873.1

We drilled two boreholes at Kekexili (8 m deep) and Fenghuoshan (6 m deep) in 1995 in the continuous permafrost regions of the QTP (Figure 1). Soil temperatures at different levels (at an interval of 0.5 m from the depth of 0.5 m to the bottom of the borehole) were measured twice per month since 1996 by a string of calibrated thermistor sensors manufactured by the State Key Laboratory of Frozen Soil Engineering, Chinese Academy of Sciences. The accuracy of the observation was estimated to less than 0.05 °C. Mean annual permafrost temperatures (MAPTs) at the depth of 6 m for both permafrost monitoring sites were analysed in this study from 1996 to 2007. Among the 16 standard meteorological stations, Wudaoliang is the closest station away from both boreholes (Figure 1), about 10 km away from Kekexili site and 54 km away from Fenghuoshan site. In consequence, we selected the MAGST series at Wudaoliang from 1996 to 2007 to study the effects of ground surface warming on the thermal regime of permafrost during the same period at the adjacent borehole observation sites Kekexili and Fenghuoshan.

In this study, when performing the calculation based on daily data we compute the annual surface freezing/thawing index as the sum of the daily GST for all days with below/above 0 °C temperatures during the freezing/thawing period. Moreover, we also define the freezing period to be June to July in the following year and the thawing period to be January to December to ensure the involvement of the entire cold and warm season in the calculation (Frauenfeld et al., 2007). We calculated the surface freezing/thawing index (DDF/DDT) using following equation:

equation image(1)
equation image(2)

where DDF and DDT are the annual surface freezing and thawing index, respectively; T represents the daily GST; MF and MT correspond to the last day of freezing and thawing periods, respectively.

The significance of statistical trends in MAGST, seasonal GST, and surface freezing/thawing index series were examined using the nonparametric Mann–Kendall trend test and Sen's slope estimate (Sen, 1968; Kendall, 1975). Mann–Kendall test is a rank-based nonparametric test, which was widely used for detecting trends in climate research because of its robustness and suitability for analysing non-Gaussian distribution data (You et al., 2008, 2010). One of the most important advantages is that Mann–Kendall test is less sensitive to outliers in observation records. In addition, least square analysis was used to analyse the effects of ground surface warming on the permafrost temperatures at Kekexili and Fenghuoshan sites.

3. Results and discussion

3.1. GST time series and trends

Table II summarizes the annual and seasonal trends for GST at the selected 16 stations over the period 1980–2007. Significant linear increases in MAGST were observed at 14 stations (significant at P < 0.01; Table II). The highest rate of increase—of 1.02 °C decade−1—was recorded for Wudaoliang station, which is located in the continuous permafrost regions. Except Shiqu station (0.34 °C decade−1, significance level at P = 0.08) which is located in the eastern discontinuous permafrost regions of QTP, all sites showed significant increases in MAGST, and the magnitudes of ground surface warming for the other 15 stations range from 0.39 to 1.02 °C decade−1 at 95% confidence level. The MAGST on the central QTP has increased by 0.60 °C decade−1 at 99.99% confidence level over the averaged time series at 16 stations in the last 28 years (Figure 2). It can be seen that an accelerating warming occurred since the mid-1990s. The trend of MAGST is much greater than previous estimation of the mean annual air temperature trends (0.2–0.4 °C decade−1 reported by Liu and Chen, 2000; Du et al., 2004; Wang et al, 2008; Rangwala et al., 2009) over the QTP during different observation periods.

Figure 2.

Time series in MAGST averaged over the selected 16 weather stations for 1980–2007 periods. The bold line is the 5-year smoothing average and the dashed line is the linear trend of MAGST

Table II. Annual and seasonal trends for GST and annual trends for air temperature (AT) as estimated from Mann–Kendall test and Sen-slope analysis for the 16 meteorological stations during the period 1980–2007
StationAnnual GSTAnnual ATSpringSummerAutumnWinter
  • Units are °C decade−1.

  • **

    Denotes significance at P < 0.01;

  • *

    denotes significance at P < 0.05.

Wudaoliang1.02**0.48**1.36**0.72**0.91**0.72**
Bange0.86**0.57**0.91**0.66**0.87**0.94**
Amdo0.62**0.45**0.70**0.58*0.84**0.69**
Naqu0.64**0.72**0.80**0.40*0.58**0.88**
Dangxiong0.49**0.49**0.44*0.100.72**0.93**
Lhasa0.62**0.70**0.47*0.070.98**0.92**
Tuotuohe0.79**0.55**0.85**0.63**0.61**0.75*
Zaduo0.79**0.61**0.73**0.55**0.55**1.23**
Qumalai0.70**0.55**0.50**0.44*0.55**1.22**
Yushu0.73**0.62**0.63**0.74**0.54**0.70*
Maduo0.49**0.61**0.71**0.53**0.61**0.12
Qingshuihe0.76**0.52**0.74**0.52**0.81**1.27**
Shiqu0.300.41**0.200.40**0.350.34
Dari0.39*0.47**0.45*0.37*0.41*0.31
Suoxian0.64**0.47**0.50**0.57**0.53**1.04**
Dingqing0.57**0.43**0.42**0.45*0.61**0.53**

Previous studies indicate that snow cover has a cooling effect on the ground surface when snow is relatively thin and air temperature fluctuates around 0 °C at the beginning of the snow accumulation in autumn (Zhang, 2005). As snow becomes thicker, the insulating effect of snow cover on ground surface increases and reaches maximum when snow is at its optimal thickness (about 40 cm). Then the insulating effect will decrease as snow thickness continues to increase. Generally, the warming or cooling effect of seasonal snow cover on the ground surface is different on daily, monthly, and annual basis (Zhang, 2005). On a daily basis, snow cover results in either warmer or cooler ground surface depending on the vriations in air temperature and the prior thermal history of the ground surface (Zhang et al., 1997). On a monthly basis, snow cover could have either positive or negative impact on the GST determined by the time of the year. The mean GST in November could be up to 20 °C warmer than the mean montly air temperature on the north slope of Alaska, while the GST was a few degrees lower than air temperature in May (Zhang et al., 1997). On an annual basis, the impact of seasonal snow cover can lead to an increase of mean ananual GST in continuous permafrost regions, even can dominate the development of permafrost in discontinuous and sporadic permafrost regions, and may reduce the seasonally freezing depth to a great degree in seasonally frozen ground regions (Zhang, 2005).

Qin et al. (2006) revealed an evident increase of annual cumulative daily snow depth (2.3% year−1) during the recent decades on the QTP in spite of a large interannual variability. It is possible that the increase of annual cumulative daily snow depth may have accelerated the ground surface warming on the QTP during the last decades. Recent studies confirmed a weakening sensible heat source over the QTP over the period 1981–2006 (Guo et al., 2011). Whereas, the larger rising rate of GST than that of air temperature means an increase of difference between GST and air temperature (Table II). Consequently, it is credible that seasonal snow cover might play a predominant role to impact the ground thermal regime on the plateau by acting as an insulator to prevent the ground from heat loss in winter. Moreover, another possible reason for the dramatic ground surface warming could owe to the increases in downward long-wave radiation illustrated by Rangwala et al. (2010).

The Sen's slope analysis shows that the warming trend of GST in winter months on the central QTP has been the largest (0.82 °C decade−1); spring and autumn have the next highest warming rate (0.67 and 0.65 °C decade−1, respectively), while summer show relatively less warming (0.50 °C decade−1); all warming trends exceed the 99.9% confidence level by Mann–Kendal test (Figure 3). The greatest ground surface warming was observed in winter at most of stations (Table II), which coincides with the trends of seasonal air temperatures over different observation periods on the QTP (Liu and Chen, 2000; Du et al., 2004; Li et al. 2010; You et al., 2010). The Pearson correlation analysis has been conducted between air and GST trends at annual and seasonal scales. It is in winter that the correlation coefficient between air temperature trends and GST trends is 0.63 (statistical significance > 99%). For spring air temperature and GST trends, the correlation coefficient is 0.45 (statistical significance > 90%). While at annual scale, in summer and autumn, the correlation coefficients are lower (<0.32) that did not exceed the 90% significance level. Similar to prominent warming of winter air temperature, the drastic increase in winter GST has occurred primarily from 2000 to 2007. This is the main reason that the warming trend in winter is the highest among four seasons on the Central QTP.

Figure 3.

Time series in seasonal GST anomaly averaged over the selected 16 weather stations for 1980–2007 periods. The bold lines represent the 5-year smoothing average

However, the winter ground surface warming was not statistically significant at Maduo, Shiqu, and Dari (Table II), which are located in the northeastern of the QTP. The possible explanation is that Northwest-Southeast inclination of microlief at those stations and their geographical locations at the edge of the anomalous atmospheric circulations may play a more important role in ground surface warming in this region (You et al., 2010). The ground surface warming in autumn and winter on the central QTP also displays a noticeably accelerating tendency since the mid-1990s, as shown in Figure 3. It is worth noting that the annual GST time series indicate an extremely low temperature in 1997 and the winter GST time series show an lowest temperature in 1998, while there was corresponding low temperature only in winter GST time series, which probably results from the heavy snow observed from September to December in this region (Yang et al., 2008).

Figure 4 shows the spatial distribution pattern of annual trends for MAGST at the selected 16 stations. About 44% of stations have large increasing trends exceeding 0.70 °C decade−1 that are statistically significant. At Wudaoliang, Tuotuohe, Qumalai, Qingshuihe, Zaduo, and Yushu, the trend for MAGST exceeds 0.70 °C decade−1, which indicates a significant ground surface warming has occurred in the hinterland of the QTP. These six stations are situated in the source regions of the Yangtze Rivers (Figure 1), while have suffered from serious environmental deterioration, such as degrading grassland ecosystems, accelerating land desertification, and reducing biodiversity in this region during the past four decades (Wang et al., 2000; Yang et al., 2007). The drastic warming in this regions could be expected to result in further eco-environmental deterioration in the future, such as extensive glaciers retreat, permafrost degradation, pasture degeneration, and severe desertification expansion.

Figure 4.

Spatial distribution pattern of annual trend slopes for MAGST at the selected 16 weather stations for 1980–2007 periods. The size of circles is proportional to the magnitude of warming trend

Figure 5 shows the spatial pattern of trends for seasonal GST at the 16 stations. In spring and autumn about 56% of stations have great increasing trends exceeding 0.60 °C decade−1 that are statistically significant. In summer, there are 25% of stations who have significantly increasing trends exceeding 0.60 °C decade−1. While in winter, 75% of stations show a significant warming whose increasing magnitude is more than 0.60 °C decade−1. Compared to the spatial pattern of trends of spring and summer GST at the 16 stations, the increase of winter GST in the source regions of the Yangtze is much more uniform on a seasonal scale. The trend of winter GST exceed 1.20 °C decade−1 at Zaduo, Qumalai, and Qingshuihe in the past 28 years (Table II), which indicated a striking warming.

Figure 5.

Spatial distribution pattern of annual trend slopes for GSTs on the seasonal scales at the selected 16 weather stations for 1980–2007 periods. The size of triangles is proportional to the magnitude of warming trend

3.2. Surface freezing/thawing index time series and trends

Figure 6 shows time series of averaged annual surface freezing/thawing index (DDF/DDT) at the selected 16 stations. Both surface freezing and thawing index time series indicate clear interannual variability during the last 28 years. The intensive warming period since the end of 1990s was characterized by an apparent decline in the surface freezing index from 1997 to 2006 and a dramatic increase in surface thawing index from 2001 to 2007. The trend analysis of annual average time series applying Mann–Kendall test and Sen's slope estimate reveals a statistically significant decrease of 111.2 °C d decade−1 (P < 0.01) for surface freezing index and a significant increase of 125.0 °C d decade−1 (P < 0.01) for surface thawing index. The significant decrease in surface freezing index and dramatic increase in surface thawing index also indicate an intensive ground surface warming during the past 28 years. The Pearson correlation analysis results indicate that there is no significant negative correlation between the trends of DDF and DDT (r = − 0.16, P = 0.55).

Figure 6.

Time series of the surface freezing/thawing index (DDF/DDT) averaged over the selected 16 weather stations for 1980–2007 periods. The dashed line is the linear trend of seasonal GST

In contrast to the pronounced winter warming in the central QTP, the winter GST has slightly decreased from 1987 to 2005 in Mongolian Plateau. Wu et al. (2011) reported that the surface freezing index has increased at a rate of 70.0 °C d decade−1 (P < 0.1), which implying a slight cooling in winter during recent 20 years in Mongolian Plateau. However, the surface thawing index has increased at a rate of 290.0 °C ddecade−1 (P < 0.01) over the period 1987–2005, which implying that summer warming is predominant in Mongolia. Compared with the tendency of air freezing/thawing index in the high-latitude regions of Northern Hemisphere reported by Frauenfeld et al. (2007), both air and surface freezing/thawing index shows similar trend of variations since 1980. While the magnitudes of trend in surface freezing and thawing index in the central QTP are both larger than those of trend in air freezing and thawing index in Northern Hemisphere (−85.6 and 44.4 °C d decade−1, respectively).

Table III describes trend magnitudes in surface freezing/thawing index estimated by Sen-slope analysis for each station. The surface freezing index at most of stations (fourteen stations) shows significant negative trends at the level of 0.05 except Shiqu and Dari. The rate of surface freezing index change varied from − 44.3 to − 181.3 °C d decade−1. While it is noted that the detected positive trends in surface thawing index are significant at the 0.01 level for all stations. The increasing rate of surface thawing index ranged from 67.1 to 200.0 °C d decade−1, which indicates a relatively higher magnitude of variation than that of surface freezing index.

Table III. Annual trends of ground surface freezing index (DDF) and ground surface thawing index (DDT) as estimated from Mann–Kendall test and Sen-slope analysis for the 16 meteorological stations during the period 1980–2007
Station nameDDF ( °C d decade−1)DDT ( °C d decade−1)
  • Units of DDF and DDT are °C d decade−1.

  • **

    Denotes significance at P < 0.01;

  • *

    denotes significance at P < 0.05.

Wudaoliang− 181.3**180.1**
Bange− 121.9**165.7**
Amdo− 86.3**143.4**
Naqu− 123.3**110.1**
Dangxiong− 83.7**87.1**
Lhasa− 44.3**200.0**
Tuotuohe− 134.4**155.6**
Zaduo− 132.1**148.9**
Qumalai− 144.5**94.5**
Yushu− 82.6**144.9**
Maduo− 75.6*112.3**
Qingshuihe− 159.0**112.2**
Shiqu− 67.267.1**
Dari− 62.283.3**
Suoxian− 127.4**108.6**
Dingqing− 60.2**124.3**

The spatial distribution of annual trends in surface freezing/thawing index (DDF/DDT) for the period 1981–2007 is shown in Figure 7. The five stations located in the source regions of Yangtze River display remarkable decrease in surface freezing index, whose dropping magnitude is greater than 100 °C d decade−1. Moreover, the surface freezing index at three stations in the south of study regions also shows a large magnitude of decline including Bange, Naqu, and Suoxian. Undoubtedly, the extensively decline in surface freezing index in the study regions will greatly weaken the potential seasonal frost penetration of ground. Similar to the characteristic of spatial variations in surface freezing index, the surface thawing index in the source regions of Yangtze River (Wudaoliang and Tuotuohe) shows substantial increase which is greater than 150 °C d decade−1, as well as the south of the study regions (Lhasa and Bange). The large magnitude of increase in surface thawing index implies a profound influence on the active layer thickness in the study regions. Both the significant decline in surface freezing index and increase in surface thawing index could be expected to accelerate the process of permafrost degradation in the central QTP.

Figure 7.

Spatial distribution pattern of annual trend slopes for surface freezing/thawing index (DDF/DDT) at the selected 16 weather stations for 1980–2007 periods. Downward triangles represent decreasing trends, and upward triangles represent increasing trends. The size of each triangle is proportional to the variation magnitude of DDF/DDT

3.3. Relationship of warming trends between MAGST and permafrost temperature series

There is a clear evidence indicated that permafrost is vulnerable to current climate change (French, 1999; Smith et al., 2005). Romanovsky et al. (2007) demonstrated that mean annual soil temperature at the depth of 1.6 m depth had risen at a trend of 0.26 °C decade−1 in East Siberia as mean annual air temperatures increased at a rate of 0.29 °C decade−1 during the period 1956–1990. On the QTP, the impacts of rising air temperature on the thermal regime of permafrost are also pronounced (Cheng and Wu, 2007; Wu and Zhang, 2008; Yang et al., 2010). Recent studies indicated the annual rate of increase in mean annual ground temperature (MAGT) amounted to 0.42–0.65 °C decade−1 for permafrost whose MAGT was lower than − 0.3 °C and 0.16–0.98 °C decade−1 for those MAGT between − 0.5 and − 3 °C, corresponding to the increasing rate of 0.24 °C decade−1 in air temperature during the period 1961–2000 (Cheng and Wu, 2007; Rangwala et al., 2009).

The linear increasing trend of MAGST for Wudaoliang amounts to 1.40 °C decade−1 over the period 1996–2007 (significant at P < 0.01, Figure 8). The linear trend is much greater than the warming during the period 1980–2007 (1.02 °C decade−1), which indicate that the ground surface warming has been accelerating since 1996. The upward trends of 0.57 and 0.54 °C decade−1 for the MAPTs at 6 m depth at Kekexili and Fenghuoshan sites, respectively were evident at the significance level of 0.01 from 1996 to 2007 (Figure 9). The comparisons between trends of mean annual ground surface and permafrost temperatures at 6 m depth show that these two parameters vary coincidently (Figures 8 and 9).

Figure 8.

Time series in MAGST ( °C) at Wudaoliang meteorological station for 1996–2007. The bold line is the 5-year smoothing average

Figure 9.

Time series in MAPT ( °C) at the depth of 6 m for 1996–2007 periods at Kekexili and Fenghuoshan site. The dashed line is the linear trend of MAPT at the depth of 6 m. Linear regression slopes, coefficient of determination (R), and significance level (P) are shown, respectively

We applied least squares method to discuss the relationship between MAGST of Wudaoliang station and MAPT at the depth of 6 m at Kekexili and Fenghuoshan. The results show that the increasing trends of permafrost temperatures are in concert with the warming GST. MAPT at the depth of 6 m are positively correlated to MAGST at Wudaoliang with R2 = 0.667 and P < 0.001 for Kekexili site and with R2 = 0.626 and P < 0.001 for Fenghuoshan site. The high correlations between the MAPT at the depth of 6 m and MAGST suggests that the changes of permafrost temperatures depends on GST to a great extent on an annual scale.

4. Conclusions

The statistical analysis of MAGST recorded at 16 sites demonstrates a statistically significant increasing trend (0.60 °C decade−1) on the central QTP during the period of 1980–2007. The magnitude of increasing GST is much greater than that of air temperature assessed in previous studies. The ground surface warming is especially drastic in the source regions of Yangtze River. Future ground surface warming could be expected to exert profound influence on the environmental changes of this region. On a seasonal scale, the MAGST for spring, summer, autumn, and winter have increased at a rate of 0.67, 0.50, 0.65, and 0.82 °C decade−1, respectively at 0.01 significance level. The winter ground surface warming for the 16 stations is especially prominent, which is generally consistent with the seasonal trends in air temperatures during different observation periods.

During recent decades, the surface freezing index has decreased at a rate of 111.2 °C d decade−1 and the surface thawing index has increased at a rate of 125.0 °C d decade−1 on the central QTP, which means a significant weakening of seasonal frost penetration and a great thickening of active layer. The decreasing/increasing trends of surface freezing/thawing index are especially prominent in the source regions of Yangtze River, which could result in extensive permafrost degradation in this region.

Statistical analysis of mean annual ground surface and permafrost temperatures shows that they are correlated. The warming rates of permafrost temperatures at the depth of 6 m at two representative sites is about 0.50–0.60 °C decade−1, corresponding to a warming rate of MAGST for 1.40 °C year−1 observed at the adjacent Wudaoliang meteorological station. The high correlations between permafrost and GST also implies that permafrost temperature is a sensitive indicator of climatic change on the central QTP, especially in the source regions of Yangtze River, and that the intensive ground surface warming is mainly responsible for the permafrost warming on the central QTP.

A possible mechanism of intensive ground surface warming could be owing to the warming impact of seasonal snow cover on the ground thermal regime. The noticeable increase in the annual cumulative daily snow depth may contribute to an increase of the mean annual GST over the QTP during the past decades, which eventually caused an increase of MAPT. However, the net effect of snow cover on ground surface and its magnitude depends on many factors, such as timing, duration, depth, density, structure, and micrometeorological conditions. And the cooling or warming effect of seasonal snow cover on ground thermal regime is variant on different time-scale basis (daily, monthly, seansonal, and annual). Caution should be exercised when explaining the possible reasons for intensive ground surface warming. Further study is needed in the following work.

Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant Numbers: 40901042; 40830533; 40871037; 41101069), the Global Change Research Program of China (2010CB951402), and the Hundred Talents Program of the Chinese Academy of Sciences (51Y251571). The instructive suggestions from two anonymous reviewers are specially appreciated. The authors also thank the National Climate Center, China Meteorological Administration, for providing the meteorological data for this study.

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